1. Field of the Invention
The invention relates to a real time radiation treatment planning system for use in effecting radiation therapy of a pre-selected anatomical portion of an animal body, comprising:
A a stepper for automatically positioning imaging means for generating image data corresponding to the anatomical portion;
B means for inserting under the guidance of a template at least one hollow needle at a position into said anatomical portion;
C radiation delivery means for defining a plurality of positions having a spatial relationship within a volume of said anatomical portion and for inserting at least one energy emitting source through said at least one hollow needle at said plurality of positions into said anatomical portion;D processing means for generating a radiation treatment plan for effecting said radiation therapy, said treatment plan including information concerning:
the number, position, direction and estimation of the best way of placement of one or more of said hollow needles within the anatomical portion and volume of said anatomical portion to be treated;
the amount of radiation dose to be emitted.
2. Discussion of the Background
The last decade has seen major changes in the way radiation treatments are delivered. The century-old objective of radiation therapy, i.e. to deliver a curative dose to the target, e.g. a tumour, while preserving normal tissues of the animal body can now be aimed at with a high degree of sophistication. However, despite of major improvements achieved with three-dimensional imaging techniques, that allow the anatomy to be properly defined, brachytherapy treatments have not yet fully benefited from these important new pieces of information.
For brachytherapy using high dose rate (HDR) energy emitting sources, catheters or hollow needles are placed in a target volume within an animal body and it is assumed that if the dose distribution covers the catheters, it should also cover the anatomy. Imaging is commonly used to set the treatment margins, but optimized dose distributions are based on considerations, such as the catheter positions and desired dose and limited to a few defined points. This necessarily results in an approximation of the shape of the anatomical portion to be treated.
For the case of treatments of the prostate, volume optimization results in a dose distribution that is essentially cylindrically shaped. With a cylindrically shaped approximation of the prostate it is possible to assure the complete coverage of the prostate volume with the radiation emitted by the source or sources. Only a conformal dose distribution delivered to the anatomical portion with an adequate margin around the prostate will encompass all affected, cancerous tissue.
The methods described in the prior art (e.g. Etienne Lessard, Med. Phys. 28. (5), May 2001) are using the concept of inverse planning to obtain an anatomy-based optimization of the dose distribution. Without any manual modification to deliver conformal HDR prostate treatment and knowing the exact location of the applicators (catheters/hollow needles), due to modern imaging techniques, it is easy to determine the possible stopping position of the radioactive source within a catheter or hollow needle present in the animal body. The possible source positions are considered given. The system has to determine based on a HDR inverse planning dose optimization governed entirely from anatomy and clinical criteria to decide the best dwell time distribution.
In U.S. Pat. No. 5,391,139 in the name of G. K. Edmundson a real time radiation treatment planning system according to the preamble is disclosed. With this system image data of the anatomical portion, e.g. the prostate is obtained for planning purposes and the medical personnel chooses an arbitrary number of needle locations using predetermined placement rules, which have been empirically determined from experience. The planning system develops a treatment plan based on these arbitrary needle positions after which the medical personnel has to examine the planning results and decide whether these results are suitable for the performing the actual radiation treatment. In case the medical personnel finds the planning results unsatisfactorily the virtual needle positions have to be altered and using the repositioned needles a new treatment plan is generated. This trial-and-error approach is repeated until a treatment plan is developed that satisfies the actual intended radiation treatment.
Subsequently the catheters or needles are inserted via a template into the animal body according to the generated treatment plan.
Conventional dose optimization algorithms are single objective, i.e. they provide a single solution. This solution is found by a trial-and-error search method as in Edmundson's U.S. Pat. No. 5,391,139, by modifying importance factors of a weighted sum of objectives, e.g. by repositioning the virtual needles or by changing the radiation dose to be delivered. This problem has been addressed currently and some methods have been proposed to find an optimal set of importance factors.
Conventional optimization methods combine the target objectives and the objectives for the surrounding healthy tissue and of critical structures into a single weighted objective function. The weight or importance factor for each objective must be supplied. The obtained solution depends on the value of importance factors used. One goal of a treatment planning system is the ability to assist the clinician in obtaining good plans on the fly. Also it should provide all the information of the possibilities given the objectives of the treatment. In order to explore the feasible region of the solution space with respect to each objective, different values for the importance factors in the aggregate objective function must be given.
Furthermore, the appropriate values of these importance factors differ from clinical case to clinical case. This implies that for any new clinical case a lot of effort is necessary for their determination.
While current optimization methods are single weighted objective methods the dose optimization problem is a true multi-objective problem and therefore multi-objective optimization methods should be used.
The gradient-based algorithm due to its efficiency allows the construction of the so-called Pareto or trade-off surface which contains all the information of the competition between the objectives which is necessary for the planner to select the solution which best fulfills his requirements.
One problem of this algorithm is that the weighted sum as used in all conventional dose optimization algorithms cannot provide solutions in possible non-convex parts of the Pareto tradeoff surface, because a convex weighted sum of objectives converges only to the convex parts of the Pareto front. Another major limitation of the algorithm is its restriction to convex objective functions for which gradients can be calculated. In this case according to the Kuhn-Tucker theorems a global optimum can be obtained and the entire Pareto front is accessible from the weighted sum.
When searching for an optimal set of importance factors dividing each importance factors in n points, then the number of combinations for k objectives is approximately proportional to nk-1 and the shape of the entire trade-off surface require a very large computational time. Most realistic problems require the simultaneous optimization of many objectives. It is unlikely that all objectives are optimal for a single set of parameters. If this is so, then there exist many, in principle infinite solutions.
A multi-objective algorithm does not provide a single solution, but a representative set of all possible solutions. Out of these representative solutions a single final solution has to be selected. It is a complex problem to automatically select such a solution and such methods have been proposed but then a planner would not know what alternatives solutions could instead be selected. In problems where different sets of objectives have to be compared this information is valuable, since it shows the possibilities a planner has for each such set.
A time analysis of the optimization with available commercial systems based on e.g. 35 clinical cases shows that even if a single optimization run requires only a few seconds the actual optimization requires 5.7±4.8 minutes. The evaluation of the results requires additional 5.8±2.5 minutes. This shows that the result of a single optimization run is not always satisfactorily and most of the time is spent in a manual trial-and-error optimization.